Effective hybrid branch-and-cut algorithm for the inventory routing problem with open vehicle routing constraints

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Expert Systems with Applications Pub Date : 2025-10-17 DOI:10.1016/j.eswa.2025.129905

Nai-Kang Yu , Bin Qian , Rong Hu , Jian-Bo Yang

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引用次数: 0

Abstract

This study considers a kind of inventory routing problem, which jointly optimizes the open vehicle routing decision and the inventory replenishment in a real-world distribution scenario. That is, the considered problem is an integrated optimization problem with two coupled subproblems (IOP_TCSP), i.e., the open vehicle routing problem (OVRP) and the inventory replenishment problem. The criterion is to minimize the total logistics and inventory costs under multiple periods. The IOP_TCSP is modelled as a mixed integer programming problem, and then a hybrid branch-and-cut algorithm combining novel Lagrangian heuristic approach and valid inequalities (HB&C_NLHAVI) is devised to deal with it. Test results on 72 instances with different scales demonstrate that the devised HB&C is more effective than the commercial solver Gurobi. Specifically, the HB&C can obviously reduce optimality gaps for many instances within the similar or less running time, and it can reduce the optimality gaps by 12–24% within only 60–70% of Gurobi’s running time for almost all large-scale instances.

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求解开放车辆路径约束下的库存路径问题的有效混合分支切断算法

本文研究了一类真实配送场景下的开放式车辆路径决策与库存补货的联合优化问题。也就是说，所考虑的问题是一个包含两个耦合子问题（IOP_TCSP）的集成优化问题，即开放式车辆路径问题（OVRP）和库存补充问题。标准是在多个时期内使物流和库存的总成本最小化。将IOP_TCSP建模为一个混合整数规划问题，然后设计了一种结合新颖的拉格朗日启发式方法和有效不等式（HB&C_NLHAVI）的混合分支切断算法来处理该问题。72个不同规模实例的测试结果表明，所设计的HB&；C比商用求解器Gurobi更有效。具体来说，HB&；C可以在相似或更短的运行时间内明显减少许多实例的最优性差距，并且可以在几乎所有大规模实例的60-70%的运行时间内将最优性差距减少12-24%。

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来源期刊

Expert Systems with Applications 工程技术-工程：电子与电气

CiteScore

13.80

自引率

10.60%

发文量

2045

审稿时长

8.7 months

期刊介绍： Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.